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Identifying unsaturated hydraulic parameters using an integrated data fusion approach on cross-borehole geophysical data.

Research output: Contribution to journalJournal article

  • Majken C. Looms
  • Andrew Binley
  • Karsten H. Jensen
  • Lars Nielsen
  • Thomas M. Hansen
<mark>Journal publication date</mark>02/2008
<mark>Journal</mark>Vadose Zone Journal
Issue number1
Number of pages11
Pages (from-to)238-248
<mark>Original language</mark>English


Cross-borehole geophysical data can provide valuable information concerning hydrologic properties of the unsaturated zone. Such data are most often used sequentially, where images of soil physical properties are obtained through numerical inversion and then converted to hydrologic state properties using petrophysical relationships. If not accounted for, inversion artifacts are transferred to the resulting hydrologic images. We propose a framework in which multiple geophysical data sets can be incorporated using an integrated data fusion approach. The geophysical data collected are integrated in a forward modeling approach to evaluate a series of plausible hydrologic models. The approach permits an evaluation of the sensitivity of geophysical data for constraining hydrologic model parameters. We illustrate the approach using geophysical data collected during a dual water and solute tracer experiment in the unsaturated zone. Cross-borehole ground penetrating radar and electrical resistivity tomography, measuring electromagnetic travel time and electrical transfer resistances, respectively, were collected during a 20-d period. As a first approximation, one-dimensional flow was considered and three models (one, two, and five layers) of the subsurface were evaluated. The five-layered model was found to be the only model capable of mimicking the infiltration pattern satisfactorily. The results showed that only the hydraulic conductivity and one of the parameters (empirical parameter n) describing the soil moisture release curve for three of the five layers could be constrained by the data, illustrating the nonuniqueness of the problem. The data fusion approach proved, however, that application of multiple geophysical methods may reduce hydraulic parameter uncertainty.